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<article article-type="research-article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="aggregator">72010350</journal-id>
      <journal-title>Color and Imaging Conference</journal-title>
      <abbrev-journal-title>color imaging conf</abbrev-journal-title>
      <issn pub-type="ppub">2166-9635</issn><issn pub-type="epub"/>
      <publisher>
        <publisher-name>Society of Imaging Science and Technology</publisher-name>
        <publisher-loc>7003 Kilworth Lane, Springfield, VA 22151, USA</publisher-loc>
      </publisher>
    </journal-meta>
    <article-meta><article-id pub-id-type="doi">10.2352/CIC.2003.11.1.art00009</article-id>
      <article-id pub-id-type="sici">2166-9635(20030101)2003:1L.42;1-</article-id>
      <article-id pub-id-type="publisher-id">cic_v2003n1/splitsection9.xml</article-id>
      <article-id pub-id-type="other">/ist/cic/2003/00002003/00000001/art00009</article-id>
      <article-categories>
        <subj-group>
          <subject>Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Biological Gain Control for High Dynamic Range Compression</article-title>
      </title-group>
      <contrib-group>
        <contrib>
          <name>
            <surname>Spitzer</surname>
            <given-names>Hedva</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Karasik</surname>
            <given-names>Yan</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Einav</surname>
            <given-names>Shmuel</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date>
        <day>01</day>
        <month>01</month>
        <year>2003</year>
      </pub-date>
      <volume>2003</volume>
      <issue>1</issue>
      <fpage>42</fpage>
      <lpage>50</lpage>
      <permissions>
        <copyright-year>2003</copyright-year>
      </permissions>
      <abstract>
        <p>The visual system has the ability to see and obtain detailed information from a highly dynamic range of a scene. For example, a person can observe items in one range by observing the inside of a dim room as well as outside through a window. An algorithm for high dynamic range compression
 that can be applied for still and video images is presented. This algorithm is based on a biological model that is also suggested for wide dynamic range and lightness constancy. It succeeded in automatically compressing the dynamic range of images to a &#x2018;human vision appearance&#x2019;
 (as is commonly required in cameras and displays) while maintaining and even improving contrast. The biological basis is expressed as retinal mechanisms of adaptation (gain control), both &#x2018;local&#x2019;, and &#x2018;remote&#x2019;, that enable also video image applications by taking into
 account the dynamics of human adaptation mechanisms. The results indicate the significant and robust contribution of adaptation mechanisms in image appearance, and have been found appropriate for next generation High dynamic range cameras (CMOS based).</p>
      </abstract>
    </article-meta>
  </front>
</article>
